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   "source": [
    "# End of week 1 exercise\n",
    "\n",
    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1070317-3ed9-4659-abe3-828943230e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from openai import OpenAI\n",
    "import os\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "\n",
    "MODEL_GPT = 'gpt-4o-mini'\n",
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up environment\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is the question; type over this to ask something new\n",
    "\n",
    "question = \"\"\"\n",
    "Please explain what this code does and why:\n",
    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "messages = [{\"role\":\"user\", \"content\":question}]\n",
    "\n",
    "openai = OpenAI()\n",
    "response = openai.chat.completions.create(model= MODEL_GPT, messages= messages, stream=True)\n",
    "\n",
    "full_reply = \"\"\n",
    "for chunk in response:\n",
    "    # For the new Chat Completions API, content is in choices[0].delta.content\n",
    "    delta = chunk.choices[0].delta\n",
    "    if delta.content:\n",
    "        text = delta.content\n",
    "        full_reply += text\n",
    "\n",
    "print(full_reply)  # final newline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "\n",
    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n",
    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')\n",
    "response = ollama.chat.completions.create(model=MODEL_LLAMA, messages=messages, stream=False)\n",
    "response.choices[0].message.content"
   ]
  }
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